Mixup for Supervision, Semi- and Self-Supervision Learning Toolbox and Benchmark

Related tags

Deep Learningopenmixup
Overview

OpenSelfSup

News

  • Downstream tasks now support more methods(Mask RCNN-FPN, RetinaNet, Keypoints RCNN) and more datasets(Cityscapes).
  • 'GaussianBlur' is replaced from Opencv to PIL, and MoCo v2 training speed doubles!
    (time/iter 0.35s-->0.16s, SimCLR and BYOL are also affected.)
  • OpenSelfSup now supports Mixed Precision Training (apex AMP)!
  • A bug of MoCo v2 has been fixed and now the results are reproducible.
  • OpenSelfSup now supports BYOL!

Introduction

The master branch works with PyTorch 1.1 or higher.

OpenSelfSup is an open source unsupervised representation learning toolbox based on PyTorch.

What does this repo do?

Below is the relations among Unsupervised Learning, Self-Supervised Learning and Representation Learning. This repo focuses on the shadow area, i.e., Unsupervised Representation Learning. Self-Supervised Representation Learning is the major branch of it. Since in many cases we do not distingush between Self-Supervised Representation Learning and Unsupervised Representation Learning strictly, we still name this repo as OpenSelfSup.

Major features

  • All methods in one repository

    For comprehensive comparison in all benchmarks, refer to MODEL_ZOO.md. Most of the selfsup pretraining methods are under the batch_size=256, epochs=200 setting.

    Method VOC07 SVM (best layer) ImageNet (best layer)
    ImageNet 87.17 76.17
    Random 30.54 16.21
    Relative-Loc 64.78 49.31
    Rotation-Pred 67.38 54.99
    DeepCluster 74.26 57.71
    NPID 74.50 56.61
    ODC 78.42 57.70
    MoCo 79.18 60.60
    MoCo v2 84.26 67.69
    SimCLR 78.95 61.57
    BYOL (epoch=300) 86.58 72.35

    • Flexibility & Extensibility

      OpenSelfSup follows a similar code architecture of MMDetection while is even more flexible than MMDetection, since OpenSelfSup integrates various self-supervised tasks including classification, joint clustering and feature learning, contrastive learning, tasks with a memory bank, etc.

      For existing methods in this repo, you only need to modify config files to adjust hyper-parameters. It is also simple to design your own methods, please refer to GETTING_STARTED.md.

    • Efficiency

      All methods support multi-machine multi-gpu distributed training.

    • Standardized Benchmarks

      We standardize the benchmarks including logistic regression, SVM / Low-shot SVM from linearly probed features, semi-supervised classification, and object detection. Below are the setting of these benchmarks.

      Benchmarks Setting Remarks
      ImageNet Linear Classification (Multi) goyal2019scaling Evaluate different layers.
      ImageNet Linear Classification (Last) MoCo Evaluate the last layer after global pooling.
      Places205 Linear Classification goyal2019scaling Evaluate different layers.
      ImageNet Semi-Sup Classification
      PASCAL VOC07 SVM goyal2019scaling Costs="1.0,10.0,100.0" to save evaluation time w/o change of results.
      PASCAL VOC07 Low-shot SVM goyal2019scaling Costs="1.0,10.0,100.0" to save evaluation time w/o change of results.
      PASCAL VOC07+12 Object Detection MoCo
      COCO17 Object Detection MoCo

    Change Log

    Please refer to CHANGELOG.md for details and release history.

    [2020-10-14] OpenSelfSup v0.3.0 is released with some bugs fixed and support of new features.

    [2020-06-26] OpenSelfSup v0.2.0 is released with benchmark results and support of new features.

    [2020-06-16] OpenSelfSup v0.1.0 is released.

    Installation

    Please refer to INSTALL.md for installation and dataset preparation.

    Get Started

    Please see GETTING_STARTED.md for the basic usage of OpenSelfSup.

    Benchmark and Model Zoo

    Please refer to MODEL_ZOO.md for for a comprehensive set of pre-trained models and benchmarks.

    License

    This project is released under the Apache 2.0 license.

    Acknowledgement

    • This repo borrows the architecture design and part of the code from MMDetection.
    • The implementation of MoCo and the detection benchmark borrow the code from moco.
    • The SVM benchmark borrows the code from fair_self_supervision_benchmark.
    • openselfsup/third_party/clustering.py is borrowed from deepcluster.

    Contributors

    We encourage researchers interested in Self-Supervised Learning to contribute to OpenSelfSup. Your contributions, including implementing or transferring new methods to OpenSelfSup, performing experiments, reproducing of results, parameter studies, etc, will be recorded in MODEL_ZOO.md. For now, the contributors include: Xiaohang Zhan (@XiaohangZhan), Jiahao Xie (@Jiahao000), Enze Xie (@xieenze), Xiangxiang Chu (@cxxgtxy), Zijian He (@scnuhealthy).

    Contact

    This repo is currently maintained by Xiaohang Zhan (@XiaohangZhan), Jiahao Xie (@Jiahao000) and Enze Xie (@xieenze).

Comments
  • [Feature] Can we load ImageNet data using LMDB?

    [Feature] Can we load ImageNet data using LMDB?

    Describe the feature

    Current ImageNet data loader can only support pillow, cv2 backends to load images. By some reasone, I need to use LMDB to load dataset which is supported by mmcls. https://github.com/Westlake-AI/openmixup/blob/8966870a05b85ea940a02c4646693ec101ab0575/openmixup/datasets/data_sources/image_list.py#L49-L51

    enhancement 
    opened by wang-tf 4
  • Question about the invTrans in the visualization code for automix

    Question about the invTrans in the visualization code for automix

    Hi, thank you for your great work. I found that the invTrans in the visualization code for automix use cifar dataset's mean & std by default. Will it work when i input the imagenet images?

    https://github.com/Westlake-AI/openmixup/blob/90dab92464e94ac5c4139c37f07ed21df9c2affb/openmixup/models/classifiers/automix_V1plus.py#L353

    help wanted 
    opened by mrbulb 4
  • How to convert model output to torchscript file?

    How to convert model output to torchscript file?

    Checklist

    • I have searched related issues but cannot get the expected help.
    • I have read related documents and don't know what to do.

    Describe the question you meet

    I already using config modified from MogaNet to train the perfect model, but I need to convert it to torchscript. How to convert the .pth to torchscript(.pt), I try the general method but get error, because the .pth file only contains the weight, and not contains the model.

    如何能转换模型文件到 TorchScript 格式？ 我已经使用修改的 MogaNet 配置文件训练出了一个良好的模型，但是我需要转换 .pth 文件到 .pt 文件，即 TorchScript 格式。 如何转换模型输出文件 .pth 到 TorchScript？我尝试了一些通用方法，但是不能成功，因为 .pth 文件仅仅包含权重，并不包含模型网络。

    help wanted 
    opened by nikbobo 3
  • Where will I get the pretrained code for moganet?

    Where will I get the pretrained code for moganet?

    Checklist

    • I have searched related issues but cannot get the expected help.
    • I have read related documents and don't know what to do.

    Describe the question you meet

    [here]

    Post related information

    1. The output of pip list | grep "mmcv\|mmcls\|^torch" [here]
    2. Your config file if you modified it or created a new one.
    [here]
    1. Your train log file if you meet the problem during training. [here]
    2. Other code you modified in the mmcls folder. [here]
    help wanted 
    opened by M-SunRise 3
  • there is a problem when i run :python setup.py develop

    there is a problem when i run :python setup.py develop

    when i run:python setup.py develop,there is a problem,i paste it down here

    running develop
C:\Users\shaoshuai\anaconda3\envs\openmixup\lib\site-packages\setuptools\command\easy_install.py:144: EasyInstallDeprecationWarning: easy_install command is deprecated. Use build and pip and other standards
-based tools.
  warnings.warn(
C:\Users\shaoshuai\anaconda3\envs\openmixup\lib\site-packages\setuptools\command\install.py:34: SetuptoolsDeprecationWarning: setup.py install is deprecated. Use build and pip and other standards-based tool
s.
  warnings.warn(
running egg_info
writing openmixup.egg-info\PKG-INFO
writing dependency_links to openmixup.egg-info\dependency_links.txt
writing requirements to openmixup.egg-info\requires.txt
writing top-level names to openmixup.egg-info\top_level.txt
reading manifest file 'openmixup.egg-info\SOURCES.txt'
adding license file 'LICENSE'
writing manifest file 'openmixup.egg-info\SOURCES.txt'
running build_ext
Creating c:\users\shaoshuai\anaconda3\envs\openmixup\lib\site-packages\openmixup.egg-link (link to .)
openmixup 0.2.5+90dab92 is already the active version in easy-install.pth

Installed d:\python_projects\openmixup
Processing dependencies for openmixup==0.2.5+90dab92
Searching for faiss-gpu==1.6.1
Reading https://pypi.org/simple/faiss-gpu/
C:\Users\shaoshuai\anaconda3\envs\openmixup\lib\site-packages\pkg_resources\__init__.py:123: PkgResourcesDeprecationWarning: gpu-1.5.3 is an invalid version and will not be supported in a future release
  warnings.warn(
C:\Users\shaoshuai\anaconda3\envs\openmixup\lib\site-packages\pkg_resources\__init__.py:123: PkgResourcesDeprecationWarning:  is an invalid version and will not be supported in a future release
  warnings.warn(
C:\Users\shaoshuai\anaconda3\envs\openmixup\lib\site-packages\pkg_resources\__init__.py:123: PkgResourcesDeprecationWarning: gpu-1.6.0 is an invalid version and will not be supported in a future release
  warnings.warn(
C:\Users\shaoshuai\anaconda3\envs\openmixup\lib\site-packages\pkg_resources\__init__.py:123: PkgResourcesDeprecationWarning: gpu-1.6.1 is an invalid version and will not be supported in a future release
  warnings.warn(
C:\Users\shaoshuai\anaconda3\envs\openmixup\lib\site-packages\pkg_resources\__init__.py:123: PkgResourcesDeprecationWarning: gpu-1.6.3 is an invalid version and will not be supported in a future release
  warnings.warn(
C:\Users\shaoshuai\anaconda3\envs\openmixup\lib\site-packages\pkg_resources\__init__.py:123: PkgResourcesDeprecationWarning: gpu-1.6.4 is an invalid version and will not be supported in a future release
  warnings.warn(
C:\Users\shaoshuai\anaconda3\envs\openmixup\lib\site-packages\pkg_resources\__init__.py:123: PkgResourcesDeprecationWarning: gpu-1.6.4.post2 is an invalid version and will not be supported in a future relea
se
  warnings.warn(
C:\Users\shaoshuai\anaconda3\envs\openmixup\lib\site-packages\pkg_resources\__init__.py:123: PkgResourcesDeprecationWarning: gpu-1.6.5 is an invalid version and will not be supported in a future release
  warnings.warn(
C:\Users\shaoshuai\anaconda3\envs\openmixup\lib\site-packages\pkg_resources\__init__.py:123: PkgResourcesDeprecationWarning: gpu-1.7.0 is an invalid version and will not be supported in a future release
  warnings.warn(
C:\Users\shaoshuai\anaconda3\envs\openmixup\lib\site-packages\pkg_resources\__init__.py:123: PkgResourcesDeprecationWarning: gpu-1.7.1 is an invalid version and will not be supported in a future release
  warnings.warn(
C:\Users\shaoshuai\anaconda3\envs\openmixup\lib\site-packages\pkg_resources\__init__.py:123: PkgResourcesDeprecationWarning: gpu-1.7.1.post1 is an invalid version and will not be supported in a future relea
se
  warnings.warn(
C:\Users\shaoshuai\anaconda3\envs\openmixup\lib\site-packages\pkg_resources\__init__.py:123: PkgResourcesDeprecationWarning: gpu-1.7.1.post2 is an invalid version and will not be supported in a future relea
se
  warnings.warn(
Downloading https://files.pythonhosted.org/packages/1d/d4/290ed049631dc061843920cd6e2b5d9af25cb5c98cb7ecbe2b7ca4bebf12/faiss-gpu-1.6.1.tar.gz#sha256=7a280e951d305d654a116b9f31275169f30841c8b851f0d689421ef8a
3ecf7b8
Best match: faiss-gpu 1.6.1
Processing faiss-gpu-1.6.1.tar.gz
Writing C:\Users\SHAOSH~1\AppData\Local\Temp\easy_install-3zs3m1q3\faiss-gpu-1.6.1\setup.cfg
Running faiss-gpu-1.6.1\setup.py -q bdist_egg --dist-dir C:\Users\SHAOSH~1\AppData\Local\Temp\easy_install-3zs3m1q3\faiss-gpu-1.6.1\egg-dist-tmp-snlcnz4n
C:\Users\shaoshuai\anaconda3\envs\openmixup\lib\site-packages\setuptools\command\install.py:34: SetuptoolsDeprecationWarning: setup.py install is deprecated. Use build and pip and other standards-based tool
s.
  warnings.warn(
Traceback (most recent call last):
  File "C:\Users\shaoshuai\anaconda3\envs\openmixup\lib\site-packages\setuptools\sandbox.py", line 156, in save_modules
    yield saved
  File "C:\Users\shaoshuai\anaconda3\envs\openmixup\lib\site-packages\setuptools\sandbox.py", line 198, in setup_context
    yield
  File "C:\Users\shaoshuai\anaconda3\envs\openmixup\lib\site-packages\setuptools\sandbox.py", line 259, in run_setup
    _execfile(setup_script, ns)
  File "C:\Users\shaoshuai\anaconda3\envs\openmixup\lib\site-packages\setuptools\sandbox.py", line 46, in _execfile
    exec(code, globals, locals)
  File "C:\Users\SHAOSH~1\AppData\Local\Temp\easy_install-3zs3m1q3\faiss-gpu-1.6.1\setup.py", line 91, in <module>

  File "C:\Users\shaoshuai\anaconda3\envs\openmixup\lib\site-packages\setuptools\__init__.py", line 87, in setup
    return distutils.core.setup(**attrs)
  File "C:\Users\shaoshuai\anaconda3\envs\openmixup\lib\site-packages\setuptools\_distutils\core.py", line 185, in setup
    return run_commands(dist)
  File "C:\Users\shaoshuai\anaconda3\envs\openmixup\lib\site-packages\setuptools\_distutils\core.py", line 201, in run_commands
    dist.run_commands()
  File "C:\Users\shaoshuai\anaconda3\envs\openmixup\lib\site-packages\setuptools\_distutils\dist.py", line 973, in run_commands
    self.run_command(cmd)
  File "C:\Users\shaoshuai\anaconda3\envs\openmixup\lib\site-packages\setuptools\dist.py", line 1217, in run_command
    super().run_command(command)
  File "C:\Users\shaoshuai\anaconda3\envs\openmixup\lib\site-packages\setuptools\_distutils\dist.py", line 992, in run_command
    cmd_obj.run()
  File "C:\Users\shaoshuai\anaconda3\envs\openmixup\lib\site-packages\setuptools\command\bdist_egg.py", line 165, in run
    cmd = self.call_command('install_lib', warn_dir=0)
  File "C:\Users\shaoshuai\anaconda3\envs\openmixup\lib\site-packages\setuptools\command\bdist_egg.py", line 151, in call_command
    self.run_command(cmdname)
  File "C:\Users\shaoshuai\anaconda3\envs\openmixup\lib\site-packages\setuptools\_distutils\cmd.py", line 319, in run_command
    self.distribution.run_command(command)
  File "C:\Users\shaoshuai\anaconda3\envs\openmixup\lib\site-packages\setuptools\dist.py", line 1217, in run_command
    super().run_command(command)
  File "C:\Users\shaoshuai\anaconda3\envs\openmixup\lib\site-packages\setuptools\_distutils\dist.py", line 992, in run_command
    cmd_obj.run()
  File "C:\Users\shaoshuai\anaconda3\envs\openmixup\lib\site-packages\setuptools\command\install_lib.py", line 11, in run
    self.build()
  File "C:\Users\shaoshuai\anaconda3\envs\openmixup\lib\site-packages\setuptools\_distutils\command\install_lib.py", line 112, in build
    self.run_command('build_ext')
  File "C:\Users\shaoshuai\anaconda3\envs\openmixup\lib\site-packages\setuptools\_distutils\cmd.py", line 319, in run_command
    self.distribution.run_command(command)
  File "C:\Users\shaoshuai\anaconda3\envs\openmixup\lib\site-packages\setuptools\dist.py", line 1217, in run_command
    super().run_command(command)
  File "C:\Users\shaoshuai\anaconda3\envs\openmixup\lib\site-packages\setuptools\_distutils\dist.py", line 992, in run_command
    cmd_obj.run()
  File "C:\Users\SHAOSH~1\AppData\Local\Temp\easy_install-3zs3m1q3\faiss-gpu-1.6.1\setup.py", line 45, in run
    sha = out.strip().decode('ascii')
  File "C:\Users\shaoshuai\anaconda3\envs\openmixup\lib\site-packages\setuptools\_distutils\command\build_ext.py", line 346, in run
    self.build_extensions()
  File "C:\Users\SHAOSH~1\AppData\Local\Temp\easy_install-3zs3m1q3\faiss-gpu-1.6.1\setup.py", line 50, in build_extensions

  File "C:\Users\SHAOSH~1\AppData\Local\Temp\easy_install-3zs3m1q3\faiss-gpu-1.6.1\setup.py", line 58, in _remove_flag
    sha = __version__.split('+')[-1]
AttributeError: 'MSVCCompiler' object has no attribute 'compiler'

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "setup.py", line 168, in <module>
    setup(
  File "C:\Users\shaoshuai\anaconda3\envs\openmixup\lib\site-packages\setuptools\__init__.py", line 87, in setup
    return distutils.core.setup(**attrs)
  File "C:\Users\shaoshuai\anaconda3\envs\openmixup\lib\site-packages\setuptools\_distutils\core.py", line 185, in setup
    return run_commands(dist)
  File "C:\Users\shaoshuai\anaconda3\envs\openmixup\lib\site-packages\setuptools\_distutils\core.py", line 201, in run_commands
    dist.run_commands()
  File "C:\Users\shaoshuai\anaconda3\envs\openmixup\lib\site-packages\setuptools\_distutils\dist.py", line 973, in run_commands
    self.run_command(cmd)
  File "C:\Users\shaoshuai\anaconda3\envs\openmixup\lib\site-packages\setuptools\dist.py", line 1217, in run_command
    super().run_command(command)
  File "C:\Users\shaoshuai\anaconda3\envs\openmixup\lib\site-packages\setuptools\_distutils\dist.py", line 992, in run_command
    cmd_obj.run()
  File "C:\Users\shaoshuai\anaconda3\envs\openmixup\lib\site-packages\setuptools\command\develop.py", line 34, in run
    self.install_for_development()
  File "C:\Users\shaoshuai\anaconda3\envs\openmixup\lib\site-packages\setuptools\command\develop.py", line 129, in install_for_development
    self.process_distribution(None, self.dist, not self.no_deps)
  File "C:\Users\shaoshuai\anaconda3\envs\openmixup\lib\site-packages\setuptools\command\easy_install.py", line 754, in process_distribution
    distros = WorkingSet([]).resolve(
  File "C:\Users\shaoshuai\anaconda3\envs\openmixup\lib\site-packages\pkg_resources\__init__.py", line 789, in resolve
    dist = best[req.key] = env.best_match(
  File "C:\Users\shaoshuai\anaconda3\envs\openmixup\lib\site-packages\pkg_resources\__init__.py", line 1075, in best_match
    return self.obtain(req, installer)
  File "C:\Users\shaoshuai\anaconda3\envs\openmixup\lib\site-packages\pkg_resources\__init__.py", line 1087, in obtain
    return installer(requirement)
  File "C:\Users\shaoshuai\anaconda3\envs\openmixup\lib\site-packages\setuptools\command\easy_install.py", line 681, in easy_install
    return self.install_item(spec, dist.location, tmpdir, deps)
  File "C:\Users\shaoshuai\anaconda3\envs\openmixup\lib\site-packages\setuptools\command\easy_install.py", line 707, in install_item
    dists = self.install_eggs(spec, download, tmpdir)
  File "C:\Users\shaoshuai\anaconda3\envs\openmixup\lib\site-packages\setuptools\command\easy_install.py", line 900, in install_eggs
    return self.build_and_install(setup_script, setup_base)
  File "C:\Users\shaoshuai\anaconda3\envs\openmixup\lib\site-packages\setuptools\command\easy_install.py", line 1174, in build_and_install
    self.run_setup(setup_script, setup_base, args)
  File "C:\Users\shaoshuai\anaconda3\envs\openmixup\lib\site-packages\setuptools\command\easy_install.py", line 1158, in run_setup
    run_setup(setup_script, args)
  File "C:\Users\shaoshuai\anaconda3\envs\openmixup\lib\site-packages\setuptools\sandbox.py", line 262, in run_setup
    raise
  File "C:\Users\shaoshuai\anaconda3\envs\openmixup\lib\contextlib.py", line 131, in __exit__
    self.gen.throw(type, value, traceback)
  File "C:\Users\shaoshuai\anaconda3\envs\openmixup\lib\site-packages\setuptools\sandbox.py", line 198, in setup_context
    yield
  File "C:\Users\shaoshuai\anaconda3\envs\openmixup\lib\contextlib.py", line 131, in __exit__
    self.gen.throw(type, value, traceback)
  File "C:\Users\shaoshuai\anaconda3\envs\openmixup\lib\site-packages\setuptools\sandbox.py", line 169, in save_modules
    saved_exc.resume()
  File "C:\Users\shaoshuai\anaconda3\envs\openmixup\lib\site-packages\setuptools\sandbox.py", line 143, in resume
    raise exc.with_traceback(self._tb)
  File "C:\Users\shaoshuai\anaconda3\envs\openmixup\lib\site-packages\setuptools\sandbox.py", line 156, in save_modules
    yield saved
  File "C:\Users\shaoshuai\anaconda3\envs\openmixup\lib\site-packages\setuptools\sandbox.py", line 198, in setup_context
    yield
  File "C:\Users\shaoshuai\anaconda3\envs\openmixup\lib\site-packages\setuptools\sandbox.py", line 259, in run_setup
    _execfile(setup_script, ns)
  File "C:\Users\shaoshuai\anaconda3\envs\openmixup\lib\site-packages\setuptools\sandbox.py", line 46, in _execfile
    exec(code, globals, locals)
  File "C:\Users\SHAOSH~1\AppData\Local\Temp\easy_install-3zs3m1q3\faiss-gpu-1.6.1\setup.py", line 91, in <module>

  File "C:\Users\shaoshuai\anaconda3\envs\openmixup\lib\site-packages\setuptools\__init__.py", line 87, in setup
    return distutils.core.setup(**attrs)
  File "C:\Users\shaoshuai\anaconda3\envs\openmixup\lib\site-packages\setuptools\_distutils\core.py", line 185, in setup
    return run_commands(dist)
  File "C:\Users\shaoshuai\anaconda3\envs\openmixup\lib\site-packages\setuptools\_distutils\core.py", line 201, in run_commands
    dist.run_commands()
  File "C:\Users\shaoshuai\anaconda3\envs\openmixup\lib\site-packages\setuptools\_distutils\dist.py", line 973, in run_commands
    self.run_command(cmd)
  File "C:\Users\shaoshuai\anaconda3\envs\openmixup\lib\site-packages\setuptools\dist.py", line 1217, in run_command
    super().run_command(command)
  File "C:\Users\shaoshuai\anaconda3\envs\openmixup\lib\site-packages\setuptools\_distutils\dist.py", line 992, in run_command
    cmd_obj.run()
  File "C:\Users\shaoshuai\anaconda3\envs\openmixup\lib\site-packages\setuptools\command\bdist_egg.py", line 165, in run
    cmd = self.call_command('install_lib', warn_dir=0)
  File "C:\Users\shaoshuai\anaconda3\envs\openmixup\lib\site-packages\setuptools\command\bdist_egg.py", line 151, in call_command
    self.run_command(cmdname)
  File "C:\Users\shaoshuai\anaconda3\envs\openmixup\lib\site-packages\setuptools\_distutils\cmd.py", line 319, in run_command
    self.distribution.run_command(command)
  File "C:\Users\shaoshuai\anaconda3\envs\openmixup\lib\site-packages\setuptools\dist.py", line 1217, in run_command
    super().run_command(command)
  File "C:\Users\shaoshuai\anaconda3\envs\openmixup\lib\site-packages\setuptools\_distutils\dist.py", line 992, in run_command
    cmd_obj.run()
  File "C:\Users\shaoshuai\anaconda3\envs\openmixup\lib\site-packages\setuptools\command\install_lib.py", line 11, in run
    self.build()
  File "C:\Users\shaoshuai\anaconda3\envs\openmixup\lib\site-packages\setuptools\_distutils\command\install_lib.py", line 112, in build
    self.run_command('build_ext')
  File "C:\Users\shaoshuai\anaconda3\envs\openmixup\lib\site-packages\setuptools\_distutils\cmd.py", line 319, in run_command
    self.distribution.run_command(command)
  File "C:\Users\shaoshuai\anaconda3\envs\openmixup\lib\site-packages\setuptools\dist.py", line 1217, in run_command
    super().run_command(command)
  File "C:\Users\shaoshuai\anaconda3\envs\openmixup\lib\site-packages\setuptools\_distutils\dist.py", line 992, in run_command
    cmd_obj.run()
  File "C:\Users\SHAOSH~1\AppData\Local\Temp\easy_install-3zs3m1q3\faiss-gpu-1.6.1\setup.py", line 45, in run
    sha = out.strip().decode('ascii')
  File "C:\Users\shaoshuai\anaconda3\envs\openmixup\lib\site-packages\setuptools\_distutils\command\build_ext.py", line 346, in run
    self.build_extensions()
  File "C:\Users\SHAOSH~1\AppData\Local\Temp\easy_install-3zs3m1q3\faiss-gpu-1.6.1\setup.py", line 50, in build_extensions

  File "C:\Users\SHAOSH~1\AppData\Local\Temp\easy_install-3zs3m1q3\faiss-gpu-1.6.1\setup.py", line 58, in _remove_flag
    sha = __version__.split('+')[-1]
AttributeError: 'MSVCCompiler' object has no attribute 'compiler'

    It looks like can't install faiss.I try to install faiss through pip command,but don't work. Please help me.

    help wanted 
    opened by 774911840 3
  • Where can we find the Resnet fine-tuning config file for A2MIM?

    Where can we find the Resnet fine-tuning config file for A2MIM?

    Checklist

    • I have searched related issues but cannot get the expected help.
    • I have read related documents and don't know what to do.

    Describe the question you meet

    In this page, I can not find the Fine-tuning Config for ResNet-50. There is a likely config(configs/benchmarks/classification/imagenet/r50_rsb_a2_ft_sz224_8xb256_cos_fp16_ep100.py) in the directory. Is it the right cofnig file?

    help wanted 
    opened by wang-tf 2
  • Update New Features and Update Documents in V0.2.6

    Update New Features and Update Documents in V0.2.6

    Updating features:

    1. Support new self-supervised method BEiT with ViT-Base on ImageNet-1K, and fix bugs of CAE, MaskFeat, and SimMIM in Dataset, Model, and Head. Note that we added HOG feature implementation borrowed from the original repo for MaskFeat.
    2. Support new backbone architecture DeiT-3 and provide configs.
    3. Update pre-training and fine-tuning config files, and documents for the relevant masked image modeling (MIM) methods (BEiT, MaskFeat, CAE, and A2MIM).
    4. Support Grad-CAM visualization tools vis_cam.py of supported architectures.

    Updating documents:

    1. Update the template and add the latest paper lists of mixup and MIM methods in Awesome Mixups and Awesome MIM.
    2. Update documents of tools.
    bug documentation enhancement 
    opened by Lupin1998 2
  • SAMix Configs for TinyImageNet

    SAMix Configs for TinyImageNet

    Hey, would it be possible to get SAMix Resnet 18 config files for TinyImageNet? I'm trying to adapt the CIFAR-100 config to Tiny but I'm running into shape mismatch issues. Thank you.

    update 
    opened by aswathn1 2
  • No module named 'openmixup'

    No module named 'openmixup'

    Hi authors, Thanks for your great work. When I run the script following your GETTING_STARTED.md, it returns me "no module named ''openmixup". When I tried to import openmixup in python directly, it tells me "no module named 'openmixup.version'". It seems that the version.py file in in the .gitignore list. Is this the reason for the error?

    bug 
    opened by xiangyu8 2
  • Refactoring and Support New Methods and Documents (V0.2.7)

    Refactoring and Support New Methods and Documents (V0.2.7)

    Code refactoring:

    1. Refactor openmixup.core (instead of openmixup.hooks) and openmixup.models.augments (contains mixup augmentation methods which are originally implemented in openmixup.models.utils). After code refactoring, the macro design of OpenMixup is similar to most projects of MMLab.
    2. Support deployment of ONNX and TorchScript in openmixup.core.export and tools/deployment. We refactored the abstract class BaseModel (implemented in openmixup/models/classifiers/base_model.py) to support forward_inference (for custom inference and visualization). We also refactored openmixup.models.heads and openmixup.models.losses to support forward_inference. You can deploy the classification models in OpenMixup according to deployment tutorials.
    3. Support testing API methods in openmixup/apis/test.py for evaluation and deployment of classification models.
    4. Refactor openmixup.core.optimizers to separate optimizers and builders and support the latest Adan optimizer.

    Supporting new features:

    1. Support detailed usage instructions in README of config files for image classification methods in configs/classification, e.g., mixups on ImageNet. We will update READMEs of other methods in configs/selfsup and configs/semisup in a few weeks.
    2. Refine the origianzation of README files according to README-Template.
    3. Support the new mixup augmentation method (AlignMix) and provide the relevant config files in various datasets.

    Updating documents:

    1. Update documents of mixup benchmarks on ImageNet in Model_Zoo_sup.md. Update config files for supported mixup methods.
    2. Update formats (figures, introductions and content tables) of awesome lists in Awesome Mixups and Awesome MIM and provide the latest methods.
    enhancement update Refactoring 
    opened by Lupin1998 1
  • Release Models of MogaNet and Update Features in V0.2.6

    Release Models of MogaNet and Update Features in V0.2.6

    Updateing new features:

    1. Fix the classification heads and update implementations and config files of AlexNet and InceptionV3.

    Uploading Benchmark Results (release):

    1. Updating mixup benchmarks on ImageNet as provided in AutoMix and update results in Model_Zoo_sup.md.
    2. Release pre-trained models and logs of MogaNet.

    Updating documents:

    1. Update documents of mixup benchmarks on ImageNet with new backbones in Model_Zoo_sup.md.
    2. Update awesome lists in Awesome Mixups and Awesome MIM and provide teaser figures of most papers as illustrations.
    documentation enhancement update 
    opened by Lupin1998 1
  • [Feature] Migration to OpenMMLab 2.0 framework.

    [Feature] Migration to OpenMMLab 2.0 framework.

    Describe the feature

    Hi, OpenMixup Team.

    We have release the OpenMMLab 2.0 framework, and release the new version of MMClassification and MMSelfSup.

    We also introduce the new features in these two blogs.

    • https://zhuanlan.zhihu.com/p/574842341
    • https://zhuanlan.zhihu.com/p/573317958

    If you think the new features are useful to your project, feel free to try or migrate to OpenMMLab 2.0 framework.

    MMClassification/MMSelfSup Team.

    enhancement 
    opened by tonysy 1
  • AutoMix tutorial that is independent of MMClassification

    AutoMix tutorial that is independent of MMClassification

    Dear Authors,

    Inspiring work! Could you please provide a simplified demo/tutorial of how to use AutoMix in general PyTorch training? I have no experience of using OpenMMLab-style framework. A small demo like google colab notebook would be of great help!

    Thanks!

    help wanted 
    opened by lofrienger 1
Releases(moganet-in1k-weights)
Owner
AI Lab, Westlake University
AI Lab, Westlake University
GitHub Repository
MIMO-UNet - Official Pytorch Implementation

MIMO-UNet - Official Pytorch Implementation This repository provides the official PyTorch implementation of the following paper: Rethinking Coarse-to-

Sungjin Cho 248 Jan 02, 2023
SynNet - synthetic tree generation using neural networks

SynNet This repo contains the code and analysis scripts for our amortized approach to synthetic tree generation using neural networks. Our model can s

Wenhao Gao 60 Dec 29, 2022
Retrieve and analysis data from SDSS (Sloan Digital Sky Survey)

Author: Behrouz Safari License: MIT sdss A python package for retrieving and analysing data from SDSS (Sloan Digital Sky Survey) Installation Install

Behrouz 3 Oct 28, 2022
In the AI for TSP competition we try to solve optimization problems using machine learning.

AI for TSP Competition Goal In the AI for TSP competition we try to solve optimization problems using machine learning. The competition will be hosted

Paulo da Costa 11 Nov 27, 2022
Neural network chess engine trained on Gary Kasparov's games.

Neural Chess It's not the best chess engine, but it is a chess engine. Proof of concept neural network chess engine (feed-forward multi-layer perceptr

3 Jun 22, 2022
"Learning Free Gait Transition for Quadruped Robots vis Phase-Guided Controller"

PhaseGuidedControl The current version is developed based on the old version of RaiSim series, and possibly requires further modification. It will be

X-Mechanics 12 Oct 21, 2022
An implementation of Geoffrey Hinton's paper "How to represent part-whole hierarchies in a neural network" in Pytorch.

GLOM An implementation of Geoffrey Hinton's paper "How to represent part-whole hierarchies in a neural network" for MNIST Dataset. To understand this

50 Oct 19, 2022
Pytorch re-implementation of Paper: SwinTextSpotter: Scene Text Spotting via Better Synergy between Text Detection and Text Recognition (CVPR 2022)

SwinTextSpotter This is the pytorch implementation of Paper: SwinTextSpotter: Scene Text Spotting via Better Synergy between Text Detection and Text R

mxin262 183 Jan 03, 2023
code for Grapadora research paper experimentation

Road feature embedding selection method Code for research paper experimentation Abstract Traffic forecasting models rely on data that needs to be sens

Eric López Manibardo 0 May 26, 2022
A project that uses optical flow and machine learning to detect aimhacking in video clips.

waldo-anticheat A project that aims to use optical flow and machine learning to visually detect cheating or hacking in video clips from fps games. Che

waldo.vision 542 Dec 03, 2022
Angular & Electron desktop UI framework. Angular components for native looking and behaving macOS desktop UI (Electron/Web)

Angular Desktop UI This is a collection for native desktop like user interface components in Angular, especially useful for Electron apps. It starts w

Marc J. Schmidt 49 Dec 22, 2022
基于Paddle框架的fcanet复现

fcanet-Paddle 基于Paddle框架的fcanet复现 fcanet 本项目基于paddlepaddle框架复现fcanet，并参加百度第三届论文复现赛，将在2021年5月15日比赛完后提供AIStudio链接～敬请期待 参考项目： frazerlin-fcanet 数据准备 本项目已挂

QuanHao Guo 7 Mar 07, 2022
Prefix-Tuning: Optimizing Continuous Prompts for Generation

Prefix Tuning Files: . ├── gpt2 # Code for GPT2 style autoregressive LM │ ├── train_e2e.py # high-level script

530 Jan 04, 2023
Finetune the base 64 px GLIDE-text2im model from OpenAI on your own image-text dataset

Finetune the base 64 px GLIDE-text2im model from OpenAI on your own image-text dataset

Clay Mullis 82 Oct 13, 2022
Implementation of GGB color space

GGB Color Space This package is implementation of GGB color space from Development of a Robust Algorithm for Detection of Nuclei and Classification of

Resha Dwika Hefni Al-Fahsi 2 Oct 06, 2021
Talk covering the features of skorch

Skorch Talk Skorch - A Union of Scikit-learn and PyTorch Presentation The slides can be downloaded at: download link. Google Colab Part One - MNIST Pa

Thomas J. Fan 3 Oct 20, 2020
Multi-Scale Geometric Consistency Guided Multi-View Stereo

ACMM [News] The code for ACMH is released!!! [News] The code for ACMP is released!!! About ACMM is a multi-scale geometric consistency guided multi-vi

Qingshan Xu 118 Jan 04, 2023
GuideDog is an AI/ML-based mobile app designed to assist the lives of the visually impaired, 100% voice-controlled

Guidedog Authors: Kyuhee Jo, Steven Gunarso, Jacky Wang, Raghav Sharma GuideDog is an AI/ML-based mobile app designed to assist the lives of the visua

Kyuhee Jo 5 Nov 24, 2021
Code for MSc Quantitative Finance Dissertation

MSc Dissertation Code ReadMe Sector Volatility Prediction Performance Using GARCH Models and Artificial Neural Networks Curtis Nybo MSc Quantitative F

2 Dec 01, 2022
计算机视觉中用到的注意力模块和其他即插即用模块PyTorch Implementation Collection of Attention Module and Plug&Play Module

PyTorch实现多种计算机视觉中网络设计中用到的Attention机制，还收集了一些即插即用模块。由于能力有限精力有限，可能很多模块并没有包括进来，有任何的建议或者改进，可以提交issue或者进行PR。

PJDong 599 Dec 23, 2022